Machine learning in software testing
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This document discusses how machine learning can be applied to various activities in software testing. It describes how machine learning works using training and test data to make predictions. Supervised and unsupervised learning techniques are discussed. Specific applications mentioned include software defect prediction, test planning, test case management, debugging, and refining blackbox test specifications. Challenges include availability of past data and finding predictable patterns, while potential steps forward include expanding machine learning to more blackbox techniques, identifying the right patterns for different test activities, algorithm analysis, and crowdsourcing.
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Machine learning in software testing
- 1. MACHINE LEARNING IN SOFTWARE TESTING Mithun Kumar S R
- 2. IDENTIFY THE MOVIE a machine can actually learn if we communicate with it
- 3. MACHINE LEARNING Machine Learning is the study of computer algorithms that improve automatically through experience - Tom Mitchell
- 4. Traditional Programming Computer Data Program Output Computer Data Machine Learning Output Program
- 5. HOW THIS WORKS Training Data Test Data Learning Machine Analyzed data for prediction
- 6. SOFTWARE TEST LIFE CYCLE Pre-execution • Test planning • Code Review • Test case management Execution • Automated run • Defect analysis Post- execution • Debugging • Regression suite update
- 7. SOFTWARE TESTING Critical task in Software development process Overspend in time and resources Automation limited to test execution
- 10. SOFTWARE TEST LIFE CYCLE Pre-execution • Test planning • Code Review • Test case management Execution • Automated run • Defect analysis Post- execution • Debugging • Regression suite update
- 11. SOFTWARE TEST ACTIVITIES AND ML Software defect prediction Test Planning Test case management Debugging
- 12. BAYESIAN ALGORITHM FOR SOFTWARE DEFECT PREDICTION
- 14. NAÏVE BAYES ALGO Branch Count LOC Defective 5 15 No 3 5 No 9 20 No 15 40 Yes 16 35 Yes Branch Count = 16 LOC = 39 C = No -> 0.000000912 C = Yes -> 0.0181 Leandru Minku: Automated Software Defect Prediction Using Machine Learning
- 15. LINEAR REGRESSION – DEFECT DENSITY http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&doc=exercises/ex2/ex2.html LOC DefectDensity
- 16. TEST PLANNING Database formation Data collection Classification of software Analyzing the results Test Cost prediction Thomas J. Cheatham, Jungsoon P. Yoo, and Nancy J. Wahl. Software testing: a machine learning experiment. Complexity Cost
- 17. MELBA – MACHINE LEARNING BASED REFINEMENT OF BLACKBOX TEST SPECIFICATION Lionel C. Briand. Novel applications of machine learning in software testing. Quality Software, International Conference on, 0:3–10, 2008.
- 18. AREAS OF APPLICATION Machine Learning-based Software Testing: Towards a Classification Framework Mahdi Noorian1, Ebrahim Bagheri1,2, and Wheichang Du1
- 19. CHALLENGES Past data availability Predictable pattern
- 20. STEPS FORWARD Black Box techniques Finding the right patterns Algorithm analysis for different types of test activity Crowdsourcing